If you have watched the movie ‘Catch Me If You Can,’ you know the ingenuity of financial fraudsters and the extent of creativity they can delve into to siphon off money. Of course, with the rise of technology, we have efficient fraud detection measures in place for financial institutions involving artificial intelligence and machine learning algorithms. Yet, fraudsters are always active in developing novel methods to commit fraud off financial institutions. In this blog, we have some new innovative suggestions to combat financial fraud, and they involve buzzing generative AI and large language models. Let us find out how!
According to a recent report from TransUnion, there has been an 80% surge in global digital fraud attempts in 2022 compared to pre-pandemic levels. Not just a rise in attempts, there is a steep rise in fraud too. As per a survey, 70% of financial organizations reported a loss of $500k in 2022 solely due to fraud. To combat such fraud, timely detection is crucial, calling for robust and innovative measures.
Where do traditional approaches fall short?
Conventional fraud detection approaches often prove inadequate to identify nuanced fraudulent activities within the vast stream of legitimate transactions. Furthermore, further complexities develop due to the sensitivity and bias in the data used for fraud detection. All of these come up as hindrances to implementing comprehensive prevention strategies. Can generative AI, the emerging technology, offer a solution that addresses these challenges?
Generative AI, a subset of artificial intelligence, can generate data or content based on the information acquired from the existing data. This technology has recently garnered much attention over its potential business applications. From creating content in the form of data, text, images, and music to streamlining processes and helping in decision-making, Gen AI is a complete game changer.
What is underlying under Generative AI?
There are these three key concepts underlying Gen AI models:
- Autoencoders
- Large language Models (LLM)
- Generative Adversarial Networks, or GANs
Autoencoders are the AI systems that compress and reconstruct data. They can efficiently compress complex data and subsequently reconstruct them with minimal loss in quality. Applicable to websites, this capability enhances speed, user experience, customer satisfaction, and sales.
Large Language Models or LLMs, are advanced AI models that can process and understand vast data sets, including financial reports, transactional data, customer communications, and online content. LLMs can analyze massive datasets seamlessly.
Generative Adversarial Network comprises two main components- a generator and a discriminator. While the generator creates new content, such as data, the discriminator evaluates the quality of the generated content by comparing it with real data.
How can we leverage GAN for Fraud detection?
GANs can generate synthetic data that closely resembles real transactions. Businesses can leverage this synthetic data to detect fraud. The generator can create artificial transaction data, and the discriminator can assess the authenticity of that and, as a result, provide high-quality, real-data-mimicking synthetic data.
How does synthetic data work for fraud detection?
Synthetic data is statistically similar to real data, though not the same. GAN algorithms leverage patterns and features inherent in the original data to generate fresh new data that closely resembles real data in its looks and behavior. Thus, it constructs a replica of the actual data without directly using the original dataset.
Regarding fraud detection, we can create a dataset of fraudulent transactions and use that to train a GAN model – generating synthetic fraudulent transactions. Financial organizations can harness this synthetic data for analytics, machine learning, and other applications.
Let us find out precisely how.
Learning from legal and illegal transactions
Since GANs can learn patterns from massive volumes of transactions, they can identify the intricate patterns that traditional models might overlook. This method can help in detecting fraudulent activities much faster and accurately. When dealing with extensive amount of transaction data and striving to safeguard the integrity of the services by preventing fraud, this approach becomes crucial.
For implementing generative AI in fraud detection, we can train the model on datasets of both legal and illegal transactions. It can learn the patterns, and we can leverage that to identify suspicious activities.
Reducing bias
Synthetic data is capable of addressing bias in fraud detection. It can create balanced datasets that accurately represent customer profiles and transaction patterns. Having AI models trained on this unbiased synthetic data can help businesses improve their fraud detection systems. Moreover, this system ensures equitable customer treatment while identifying and preventing fraud.
Generates user-friendly explanations
Since generative AI can simplify the fraud explanations for each unique use case, it makes complex use cases easy to understand for everyone. With comprehensive and clear insights, it is easier to convey the decision-makers, investigators and customers that why a claim is flagged. Using generative AI, businesses can in fact implement more transparency in their algorithm bases fraud detection systems. Ultimately, it leads to enhanced decision-making and better trust among the involved parties.
Gen AI can assist with Data Privacy and protection.
Data availability is scarce in sensitive industries such as finance or healthcare, mainly due to data privacy regulations. Although data privacy is imperative to protect sensitive private customer data, it is also a major hurdle to build robust AI models without available real data. Synthetic data can come to the rescue here. It can provide real-world high-quality data (mimicking the exact real data) and mitigate compliance hurdles.
The synthetic data does not have any identifiable information or real sensitive details. This, in turn, reduces the risk of costly data breaches.
Large language models (LLMs) for fraud detection
LLMs have the capability to process and understand vast amounts of data. Analyzing data sources inclusive of not only transaction records and financial reports, but also customer communications and online content, LLMs can detect suspicious patterns and unusual activities which are subtle and challenging to find out. LLMs can also process unstructured data and contextual information- making it a fitting response to the ever-evolving fraudulent tactics.
Let us understand this with an example. With the steep in online shopping, the risk of fraudulent transactions also rises. While the traditional ML models can flag large and suspicious purchases, LLMs can scrutinize consistent buying behaviors, track down the patterns, and even analyze the language a customer uses when giving feedback. Together, these can help detect minute or subtle patterns that may easily go amiss and uncover potential fraud. Similarly, in other use cases, LLMS can delve deeper and keep a vigilant eye on minute suspicious patterns that are usually challenging to practice.
How can we help?
Generative AI has multiple potentials when it comes to fraud detection. Its capability to create synthetic data emerges as a promising solution to the immense challenges that finance firms face in fraud detection. Similarly, we also saw how large language models or LLMs can do wonders when it comes to detecting fraud amidst a myriad of data sources and variety of data – all because of it capability to process vast amounts of data. Harnessing this gen AI technology will prove crucial in the evolving finance sector. An environment that fosters fairness, accuracy, privacy and understanding – will usher in a new era of fraud detection.
If you are looking to leverage the powers of generative AI in finance or for fraud detection, our team of experts at Saxon AI will be glad to help. We understand that every business is unique, and we specialize in tailoring a solution for your business needs. Book a consultation with our Gen AI experts now.
